Department Chair of Artificial Intelligence and Human Health at Mount Sinai -- Founder of Paige
A #FoundationModel is just the first step in revolutionizing #AI for #pathology. But the big question remains: How can we effectively aggregate thousands of tile embeddings for a whole slide? Here's the answer, hot off the press by my exceptional PhD student Shengjia Chen and team: 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐏𝐚𝐭𝐡𝐨𝐥𝐨𝐠𝐲: 𝐀 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 arXiv: https://lnkd.in/dUnx6QqT 🔍 Key Insights: 1️⃣ 𝐃𝐨𝐦𝐚𝐢𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐌𝐨𝐝𝐞𝐥𝐬: We found that embeddings derived from histological image-trained foundation models outperform those based on ImageNet models. 2️⃣ 𝐒𝐩𝐚𝐭𝐢𝐚𝐥-𝐀𝐰𝐚𝐫𝐞 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧: While spatial-aware aggregators enhance performance with ImageNet models, they offer less improvement when using pathology foundation models. 3️⃣ 𝐍𝐨 𝐎𝐧𝐞-𝐒𝐢𝐳𝐞-𝐅𝐢𝐭𝐬-𝐀𝐥𝐥: No single aggregation method excels across all tasks, highlighting the need for adaptable techniques in clinical AI. 👉️ 𝐁𝐨𝐧𝐮𝐬: Checkout the research landscape map to see the evolution of aggregation methods over time! This work took a village, I am deeply grateful to have been part of: Shengjia Chen, Gabriele Campanella, Abdulkadir Elmas, Aryeh Stock, Jennifer Z., Alexandros D. Polydorides, Adam Schoenfeld, Kuan-lin Huang, Jane Houldsworth, Chad Vanderbilt #computationalpathology #histology #computervision #machinelearning #biotech #healthcare #deeplearning
Thanks for sharing
Congratulations Shengjia Chen !! Very cool work!
Rizwan Qureshi
1mo
Thanks for sharing! Great
Mohsin Bilal
1mo
Interesting! Thanks for sharing.
Simon Castillo
1mo
Great!
Senior Director-Digital Pathology Leader-Science Fellow/Oncology Precision Medicine at Bayer Pharmaceuticals
1moNice! Thanks for sharing Thomas Fuchs !